varycoef: Modeling Spatially Varying Coefficients

Implements a maximum likelihood estimation (MLE) method for estimation and prediction of Gaussian process-based spatially varying coefficient (SVC) models (Dambon et al. (2021a) <doi:10.1016/j.spasta.2020.100470>). Covariance tapering (Furrer et al. (2006) <doi:10.1198/106186006X132178>) can be applied such that the method scales to large data. Further, it implements a joint variable selection of the fixed and random effects (Dambon et al. (2021b) <arXiv:2101.01932>). The package and its capabilities are described in (Dambon et al. (2021c) <arXiv:2106.02364>).

Version: 0.3.3
Depends: R (≥ 3.5.0), spam
Imports: glmnet, lhs, methods, mlr, mlrMBO, optimParallel (≥ 0.8-1), ParamHelpers, pbapply, smoof
Suggests: DiceKriging, gstat, parallel, spData, sp
Published: 2022-05-31
Author: Jakob A. Dambon ORCID iD [aut, cre], Fabio Sigrist ORCID iD [ctb], Reinhard Furrer ORCID iD [ctb]
Maintainer: Jakob A. Dambon <jakob.dambon at>
License: GPL-2
NeedsCompilation: no
Citation: varycoef citation info
CRAN checks: varycoef results


Reference manual: varycoef.pdf


Package source: varycoef_0.3.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): varycoef_0.3.3.tgz, r-oldrel (arm64): varycoef_0.3.3.tgz, r-release (x86_64): varycoef_0.3.3.tgz, r-oldrel (x86_64): varycoef_0.3.3.tgz
Old sources: varycoef archive


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